Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T05:16:05.811Z Has data issue: false hasContentIssue false
  • English
  • Français

Neighborhood deprivation and depression in adult twins: genetics and gene×environment interaction

Published online by Cambridge University Press:  09 November 2016

E. Strachan ,
G. Duncan ,
E. Horn  and
E. Turkheimer
E. Strachan*
Affiliation:
University of Washington, Seattle, WA, USA
G. Duncan
Affiliation:
Washington State University, Spokane, WA, USA
E. Horn
Affiliation:
University of Virginia, Charlottesville, VA, USA
E. Turkheimer
Affiliation:
University of Virginia, Charlottesville, VA, USA
*
*Address for correspondence: E. Strachan, PhD, University of Washington School of Medicine, University of Washington, Seattle, WA 98195, USA. (Email: erstrach@uw.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Depression is a significant problem and it is vital to understand its underlying causes and related policy implications. Neighborhood characteristics are implicated in depression but the nature of this association is unclear. Unobserved or unmeasured factors may confound the relationship. This study addresses confounding in a twin study investigating neighborhood-level effects on depression controlling for genetics, common environment, and gene×environment (G × E) interactions.

Method

Data on neighborhood deprivation and depression were gathered from 3155 monozygotic twin pairs and 1275 dizygotic pairs (65.7% female) between 2006 and 2013. The variance for both depression and neighborhood deprivation was decomposed into three components: additive genetic variance (A); shared environmental variance (C); and non-shared environmental variance (E). Depression was then regressed on neighborhood deprivation to test the direct association and whether that association was confounded. We also tested for a G × E interaction in which the heritability of depression was modified by the level of neighborhood deprivation.

Results

Depression and neighborhood deprivation showed evidence of significant A (21.8% and 15.9%, respectively) and C (13.9% and 32.7%, respectively) variance. Depression increased with increasing neighborhood deprivation across all twins (p = 0.009), but this regression was not significant after controlling for A and C variance common to both phenotypes (p = 0.615). The G × E model showed genetic influences on depression increasing with increasing neighborhood deprivation (p < 0.001).

Conclusions

Neighborhood deprivation is an important contributor to depression via increasing the genetic risk. Modifiable pathways that link neighborhoods to depression have been proposed and should serve as targets for intervention and research.

Keywords

Depressiongene×environmentgeneticsneighborhood deprivationtwins
Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 4 , March 2017 , pp. 627 - 638
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Afari, N, Noonan, C, Goldberg, J, Edwards, K, Gadepalli, K, Osterman, B, Evanoff, C, Buchwald, D (2006). University of Washington Twin Registry: construction and characteristics of a community-based twin registry. Twin Research and Human Genetics 9, 1023–1029.CrossRefGoogle ScholarPubMed
Aneshensel, CS, Sucoff, CA (1996). The neighborhood context of adolescent mental health. Journal of Health and Social Behavior 37, 293–310.CrossRefGoogle ScholarPubMed
Aneshensel, CS, Wight, RG, Miller-Martinez, D, Botticello, AL, Karlamangla, AS, Seeman, TE (2007). Urban neighborhoods and depressive symptoms among older adults. Journal of Gerontology Series B: Psychological Sciences and Social Sciences 62, S52–S59.CrossRefGoogle ScholarPubMed
Attar, BK, Guerra, NG, Tolan, PH (1994). Neighborhood disadvantage, stressful life events and adjustments in urban elementary-school children. Journal of Clinical Child Psychology 23, 391–400.CrossRefGoogle Scholar
Beard, JR, Cerdá, M, Blaney, S, Ahern, J, Vlahov, D, Galea, S (2009). Neighborhood characteristics and change in depressive symptoms among older residents of New York City. American Journal of Public Health 99.CrossRefGoogle ScholarPubMed
Belden Russonello & Stewart LLC (2011). The 2011 Community Preference Survey: What Americans are Looking for when Deciding where to Live. Washington, DC.Google Scholar
Blair, A, Ross, NA, Gariepy, G, Schmitz, N (2014). How do neighborhoods affect depression outcomes? A realist review and a call for the examination of causal pathways. Social Psychiatry and Psychiatric Epidemiology 49, 873–887.CrossRefGoogle Scholar
Bocquier, A, Cortaredona, S, Verdoux, H, Sciortino, V, Nauleau, S, Verger, P (2013). Social inequalities in new antidepressant treatment: a study at the individual and neighborhood levels. Annals of Epidemiology 23, 99–105.CrossRefGoogle ScholarPubMed
Chase-Lansdale, PL, Gordon, RA (1996). Economic hardship and the development of five-and six-year-olds: neighborhood and regional perspectives. Child Development 67, 3338–3367.CrossRefGoogle Scholar
Chisholm, D, Sanderson, K, Ayuso-Mateos, JL, Saxena, S (2004). Reducing the global burden of depression: population-level analysis of intervention cost-effectiveness in 14 world regions. British Journal of Psychiatry 184, 393–403.CrossRefGoogle ScholarPubMed
Duncan, GE, Dansie, EJ, Strachan, E, Munsell, M, Huang, R, Vernez Moudon, A, Goldberg, J, Buchwald, D (2012). Genetic and environmental influences on residential location in the US. Health & Place 18, 515–519.CrossRefGoogle ScholarPubMed
Duncan, GE, Mills, B, Strachan, E, Hurvitz, P, Huang, R, Moudon, AV, Turkheimer, E (2014). Stepping towards causation in studies of neighborhood and environmental effects: how twin research can overcome problems of selection and reverse causation. Health Place 27C, 106–111.CrossRefGoogle Scholar
Eaves, LJ, Verhulst, B (2014). Problems and pit-falls in testing for G x E and epistasis in candidate gene studies of human behavior. Behavior Genetics 44, 578–590.CrossRefGoogle Scholar
Edwards, B, Bromfield, LM (2009). Neighborhood influences on young children's conduct problems and pro-social behavior: evidence from an Australian national sample. Children and Youth Services Review 31, 317–324.CrossRefGoogle Scholar
Eisen, S, Neuman, R, Goldberg, J, Rice, J, True, W (1989). Determining zygosity in the Vietnam Era Twin Registry: an approach using questionnaires. Clinical Genetics 35, 423–432.CrossRefGoogle ScholarPubMed
Everson-Rose, SA, Skarupski, KA, Barnes, LL, Beck, T, Evans, DA, Mendes de Leon, CF (2011). Neighborhood socioeconomic conditions are associated with psychosocial functioning in older black and white adults. Health & Place 17, 793–800.CrossRefGoogle ScholarPubMed
Galea, S, Ahern, J, Nandi, A, Tracy, M, Beard, J, Vlahov, D (2007). Urban neighborhood poverty and the incidence of depression in a population-based cohort study. Annals of Epidemiology 17, 171–179.CrossRefGoogle Scholar
Henderson, C, Roux, AVD, Jacobs, DR, Kiefe, CI, West, D, Williams, DR (2005). Neighbourhood characteristics, individual level socioeconomic factors, and depressive symptoms in young adults: the CARDIA study. Journal of Epidemiology and Community Health 59, 322–328.CrossRefGoogle ScholarPubMed
Johnson, W (2007). Genetic and environmental influences on behavior: capturing all the interplay. Psychological Review 114, 423–440.CrossRefGoogle ScholarPubMed
Joseph, J (2002). Twin studies in psychiatry and psychology: science or pseudoscience? Psychiatric Quarterly 73, 71–82.CrossRefGoogle ScholarPubMed
Kind, AJ, Jencks, S, Brock, J, Yu, M, Bartels, C, Ehlenbach, W, Greenberg, C, Smith, M (2014). Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Annals of Internal Medicine 161, 765–74.CrossRefGoogle ScholarPubMed
Kendler, KS, Neale, MC, Kessler, RC, Heath, AC, Eaves, LJ (1994). Parental treatment and the equal environment assumption in twin studies of psychiatric illness. Psychologocal Medicine 24, 579–590.CrossRefGoogle ScholarPubMed
Kessler, RC, Berglund, P, Demler, O, Jin, R, Koretz, D, Merikangas, KR, Rush, J, Walters, EE, Wang, PS (2003). The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication. Journal of the American Medical Association 289, 3095–3105.CrossRefGoogle ScholarPubMed
Kroenke, K, Spitzer, RL, Williams, JB (2003). The patient health questionnaire-2: validity of a two-item depression screener. Medical Care 41, 1284–1292.CrossRefGoogle ScholarPubMed
Kroenke, K, Spitzer, RL, Williams, W (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 16, 606–616.CrossRefGoogle ScholarPubMed
Kubzansky, LD, Subramanian, S, Kawachi, I, Fay, ME, Soobader, M-J, Berkman, LF (2005). Neighborhood contextual influences on depressive symptoms in the elderly. American Journal of Epidemiology 162, 253–260.CrossRefGoogle ScholarPubMed
Mair, C, Diez Roux, AV, Shen, M, Shea, S, Seeman, TE, Echeverria, S, O'Meara, ES (2009). Cross-sectional and longitudinal associations of neighborhood cohesion and stressors with depressive symptoms in the multiethnic study of atherosclerosis. Annals of Epidemiology 19, 49–57.CrossRefGoogle ScholarPubMed
Mair, C, Roux, AV, Galea, S (2008). Are neighbourhood characteristics associated with depressive symptoms? A review of evidence. British Medical Journal 62, 940–946.Google ScholarPubMed
Matheson, FI, Moineddin, R, Dunn, JR, Creatore, MI, Gozdyra, P, Glazier, RH (2006). Urban neighborhoods, chronic stress, gender and depression. Social Science & Medicine 63, 2604–2616.CrossRefGoogle ScholarPubMed
Mitchell, KS, Mazzeo, SE, Bulik, CM, Aggen, SH, Kendler, KS, Neale, MC (2007). An investigation of a measure of twins’ equal environments. Twin Research and Human Genetics 10, 840–847.CrossRefGoogle ScholarPubMed
Molenaar, D, Dolan, CV (2014). Testing systematic genotype by environment interactions using item level data. Behavior Genetics 44, 212–231.CrossRefGoogle ScholarPubMed
Murata, C, Kondo, K, Hirai, H, Ichida, Y, Ojima, T (2008). Association between depression and socio-economic status among community-dwelling elderly in Japan: the Aichi Gerontological Evaluation Study (AGES). Health & Place 14, 406–414.CrossRefGoogle ScholarPubMed
Muthén, LK, Muthén, BO (1998–2012). Mplus User's Guide, 7th edn. Muthén & Muthén: Los Angeles.Google Scholar
Muthén, LK, Muthén, BO (2014). Mplus Version 7.3. Muthén & Muthén: Los Angeles.Google Scholar
Neale, MC, Maes, HH (2004). Methodology for Genetic Studies of Twins and Families. Kluwer: Dordrecht, The Netherlands.Google Scholar
Ostir, GV, Eschbach, K, Markides, KS & Goodwin, JS (2003). Neighbourhood composition and depressive symptoms among older Mexican Americans. Journal of Epidemiology and Community Health 57, 987–992.CrossRefGoogle ScholarPubMed
Puentes, R (2015). Why infrastructure matters: rotten roads, bum economy. In: Metropolitan infrastructure initiative. Washington Examiner (http://www.brookings.edu/research/opinions/2015/01/20-infrastructure-matters-roads-economy-puentes).Google Scholar
Purcell, S (2002). Variance components models for gene-environment interaction in twin analysis. Twin Research 5, 554–571.CrossRefGoogle ScholarPubMed
Richardson, R, Westley, T, Gariépy, G, Austin, N, Nandi, A (2015). Neighborhood socioeconomic conditions and depression: a systematic review and meta-analysis. Social Psychiatry and Psychiatric Epidemiology 50, 1–16.CrossRefGoogle ScholarPubMed
Robert, SA (1999). Socioeconomic position and health: the independent contribution of community socioeconomic context. Annual Review of Sociology 896, 489–516.CrossRefGoogle Scholar
Ross, CE (2000). Neighborhood disadvantage and adult depression. Journal of Health and Social Behavior 51, 177–187.CrossRefGoogle Scholar
Sariaslan, A, Larsson, H, D'Onofrio, B, Langstrom, N, Fazel, S, Lichtenstein, P (2015). Does population density and neighborhood deprivation predict schizophrenia? A nationwide Swedish family-based study of 2.4 million individuals. Schizophrenia Bulletin 41, 494–502.CrossRefGoogle ScholarPubMed
Schwabe, I, van den Berg, SM (2014). Assessing genotype by environment interaction in case of heterogeneous measurement error. Behavior Genetics 44, 394–406.Google ScholarPubMed
Silver, E, Mulvey, EP, Swanson, JW (2002). Neighborhood structural characteristics and mental disorder: faris and Dunham revisited. Social Science & Medicine 55, 1457–1470.CrossRefGoogle ScholarPubMed
Singh, GK (2003). Area deprivation and widening inequalities in US Mortality, 1969–1998. American Journal of Public Health 93, 1137–1143.CrossRefGoogle ScholarPubMed
Skapinakis, P, Lewis, G, Araya, R, Jones, K, Williams, G (2005). Mental health inequalities in Wales, UK: multi-level investigation of the effect of area deprivation. British Journal of Psychiatry 186, 417–422.CrossRefGoogle ScholarPubMed
Spitz, E, Moutier, R, Reed, T, Busnel, MC, Marchaland, C, Roubertoux, PL, Carlier, M (1996). Comparative diagnoses of twin zygosity by SSLP variant analysis, questionnaire, and dermatoglyphic analysis. Behavioral Genetics 26, 55–63.CrossRefGoogle ScholarPubMed
State of Washington (2013). Median Household Income Estimates by County: 1989 to 2013 and Projection for 2014. State of Washington Office of Financial Management: Olympia, WA.Google Scholar
Strachan, E, Hunt, C, Afari, N, Duncan, G, Noonan, C, Schur, E, Watson, N, Goldberg, J, Buchwald, D (2013). The University of Washington Twin Registry: poised for the next generation of twin research. Twin Research and Human Genetics 16, 455–462.CrossRefGoogle ScholarPubMed
Torgersen, S (1979). The determination of twin zygosity by means of a mailed questionnaire. Acta Geneticae Medicae et Gemellologiae 28, 225–236.CrossRefGoogle ScholarPubMed
Turkheimer, E, Harden, KP (2014). Behavior genetic research methods. In Handbook of Research Methods in Social and Personality Psychology (ed. Reis, H. T. and Judd, C. M.), pp. 159–187. Cambridge University Press: Cambridge, UK.CrossRefGoogle Scholar
van der Sluis, S, Posthuma, D, Dolan, CV (2012). A note on false positives and power in G x E modelling of twin data. Behavior Genetics 42, 170–186.CrossRefGoogle Scholar
Van Hulle, CA, Rathouz, PJ (2015). Operating characteristics of statistical methods for detecting gene-by-measured-environment interaction in the presence of gene-environment correlation under violations of distributional assumptions. Twin Research and Human Genetics 18, 19–27.CrossRefGoogle Scholar
Wainwright, NW, Surtees, PG (2004). Area and individual circumstances and mood disorder prevalence. The British Journal of Psychiatry 185, 227–232.CrossRefGoogle ScholarPubMed
Ware, EB, Smith, JA, Mukherjee, B, Lee, S, Kardia, SLR, Diez-Roux, AVD (2015). Applying novel methods for assessing individual- and neighborhood-level social and psychosocial environment interactions with genetic factors in the prediction of depressive symptoms in the multi-ethnic study of atherosclerosis. Behavior Genetics 46, 89–99.CrossRefGoogle ScholarPubMed
Watson, NF, Harden, KP, Buchwald, D, Vitiello, MV, Pack, AI, Strachan, E, Goldberg, J (2014). Sleep duration and depressive symptoms: a gene-environment interaction. Sleep 37, 351–358.CrossRefGoogle ScholarPubMed
Watson, NF, Horn, E, Duncan, GE, Buchwald, D, Vitiello, MV, Turkheimer, E (2016). Sleep duration and area-level deprivation in twins. Sleep 39, 67–77.CrossRefGoogle ScholarPubMed
Weich, S, Twigg, L, Lewis, G, Jones, K (2005). Geographical variation in rates of common mental disorders in Britain: prospective cohort study. British Journal of Psychiatry 187, 29–34.CrossRefGoogle ScholarPubMed
Wight, RG, Ko, MJ, Aneshensel, CS (2011). Urban neighborhoods and depressive symptoms in late middle age. Research on Aging 33, 28–50.CrossRefGoogle ScholarPubMed
Xue, Y, Leventhal, T, Brooks-Gunn, J, Earls, FJ (2005). Neighborhood residence and mental health problems of 5- to 11-year-olds. Archives of General Psychiatry 62, 554.CrossRefGoogle ScholarPubMed
Yen, IH, Kaplan, GA (1999). Poverty area residence and changes in depression and perceived health status: evidence from the Alameda County Study. International Journal of Epidemiology 28, 90–94.CrossRefGoogle ScholarPubMed